In this article, we present a recent trend of approaches, hereafter referred to as Collect4NLP, and discuss its applicability. Collect4NLP-based approaches collect inputs from language learners through learning exercises and aggregate the collected data to derive linguistic knowledge of expert quality. The primary purpose of these approaches is to improve NLP resources, however sincere concern with the needs of learners is crucial for making Collect4NLP work. We discuss the applicability of Collect4NLP approaches in relation to two perspectives. On the one hand, we compare Collect4NLP approaches to the two crowdsourcing trends currently most prevalent in NLP, namely Crowdsourcing Platforms (CPs) and Games-With-A-Purpose (GWAPs), and identify strengths and weaknesses of each trend. By doing so we aim to highlight particularities of each trend and to identify in which kind of settings one trend should be favored over the other two. On the other hand, we analyze the applicability of Collect4NLP approaches to the production of different types of NLP resources. We first list the types of NLP resources most used within its community and second propose a set of blueprints for mapping these resources to well-established language learning exercises as found in standard language learning textbooks.

CLARIN is a European Research Infrastructure providing access to digital language resources and tools from across Europe and beyond to researchers in the humanities and social sciences. This paper focuses on CLARIN as a platform for the sharing of language resources. It zooms in on the service offer for the aggregation of language repositories and the value proposition for a number of communities that benefit from the enhanced visibility of their data and services as a result of integration in CLARIN. The enhanced findability of language resources is serving the social sciences and humanities (SSH) community at large and supports research communities that aim to collaborate based on virtual collections for a specific domain. The paper also addresses the wider landscape of service platforms based on language technologies which has the potential of becoming a powerful set of interoperable facilities to a variety of communities of use.

We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach by explaining its core paradigm that consists in pairing specific types of LRs with specific exercises, by detailing both its strengths and challenges, and by discussing how much these challenges have been addressed at present. Accordingly, we also report on on-going proof-of-concept efforts aiming at developing the first prototypical implementation of the approach in order to correct and extend an LR called ConceptNet based on the input crowdsourced from language learners. We then present an international network called the European Network for Combining Language Learning with Crowdsourcing Techniques (enetCollect) that provides the context to accelerate the implementation of this generic approach. Finally, we exemplify how it can be used in several language learning scenarios to produce a multitude of NLP resources and how it can therefore alleviate the long-standing NLP issue of the lack of LRs.

In this work, we report on a crowdsourcing experiment conducted using the V-TREL vocabulary trainer which is accessed via a Telegram chatbot interface to gather knowledge on word relations suitable for expanding ConceptNet. V-TREL is built on top of a generic architecture implementing the implicit crowdsourding paradigm in order to offer vocabulary training exercises generated from the commonsense knowledge-base ConceptNet and – in the background – to collect and evaluate the learners’ answers to extend ConceptNet with new words. In the experiment about 90 university students learning English at C1 level, based on Common European Framework of Reference for Languages (CEFR), trained their vocabulary with V-TREL over a period of 16 calendar days. The experiment allowed to gather more than 12,000 answers from learners on different question types. In this paper we present in detail the experimental setup and the outcome of the experiment, which indicates the potential of our approach for both crowdsourcing data as well as fostering vocabulary skills.

The major European language infrastructure initiatives like CLARIN (Hinrichs and Krauwer, 2014), DARIAH (Edmond et al., 2017) or Europeana (Europeana Foundation, 2015) have been built by focusing in the first place on institutions of larger scale, like specialized research departments and larger official units like national libraries, etc. However, besides these principal players also a large number of smaller language actors could contribute to and benefit from language infrastructures. Especially since these smaller institutions, like local libraries, archives and publishers, often collect, manage and host language resources of particular value for their geographical and cultural region, it seems highly relevant to find ways of engaging and connecting them to existing European infrastructure initiatives. In this article, we first highlight the need for reaching out to smaller local language actors and discuss challenges related to this ambition. Then we present the first step in how this objective was approached within a local language infrastructure project, namely by means of a structured documentation of the local language actors landscape in South Tyrol. We describe how the documentation efforts were structured and organized, and what tool we have set up to distribute the collected data online, by adapting existing CLARIN solutions.

In this paper, we present our work on developing a vocabulary trainer that uses exercises generated from language resources such as ConceptNet and crowdsources the responses of the learners to enrich the language resource. We performed an empirical evaluation of our approach with 60 non-native speakers over two days, which shows that new entries to expand Concept-Net can efficiently be gathered through vocabulary exercises on word relations. We also report on the feedback gathered from the users and an expert from language teaching, and discuss the potential of the vocabulary trainer application from the user and language learner perspective. The feedback suggests that v-trel has educational potential, while in its current state some shortcomings could be identified.